Paper: arXiv 2403.16525

Authors: Eva Lütkebohmert, Julian Sester

Abstract

We propose a new deep learning approach for the quantification of name concentration risk in loan portfolios. Our approach is tailored for small portfolios and allows for both an actuarial as well as a mark-to-market definition of loss. The training of our neural network relies on Monte Carlo simulations with importance sampling which we explicitly formulate for the CreditRisk${+}$ and the ratings-based CreditMetrics model. Numerical results based on simulated as well as real data demonstrate the accuracy of our new approach and its superior performance compared to existing analytical methods for assessing name concentration risk in small and concentrated portfolios.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces an innovative deep learning framework incorporating Monte Carlo importance sampling for credit portfolio concentration risk. It effectively addresses limitations in traditional analytical models for small portfolios, backed by solid mathematical formulation and both simulated and real-world empirical validation.

Research Flowchart

  flowchart TD
    A[Goal: Quantify Name Concentration Risk<br>in Small Loan Portfolios] --> B[Data & Inputs:<br>Simulated & Real Loan Portfolios]
    B --> C[Methodology: Formulate Loss Models<br>CreditRisk+ & CreditMetrics]
    C --> D[Computational Process:<br>Monte Carlo Simulations with Importance Sampling]
    D --> E[Deep Learning:<br>Train Neural Network on Simulated Loss Data]
    E --> F[Outcomes: Superior Accuracy over<br>Analytical Methods for Small Portfolios]

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1 of 103 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.